library(Hmisc)
library(tidyverse)Homework 1
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Problem 1
Survey
Sunday, September 1st at 9:03pm
Campuswire
Problem 2
Question 1
The study population consists of individuals aged 16 and older living in private households in England and Whales. The study population includes all crimes reported and investigated by the UK police.
Question 2
A survey sampling method is used, where approximately 38,000 individuals are selected to self report their experiences with crime. Administrative records of all crimes investigated by the police are used, implying a non-sampling, comprehensive data collection approach.
Question 3
The sampled population consists of individuals aged 16 and over who are not in communal living situations, and who participated in the survey. The sampled population includes all criminal incidents investigated and recorded by the UK police based on internal crime definitions.
Question 4
The target population for both data sets is the entire population of England an Wales, including all people who may experience crime.
Question 5
Data Set 1 may face reliability issues due to self-reported responses, which can vary based on individual recall or perception. Data Set 2 is likely more reliable because it involves police records, though inconsistencies may arise from varying police practices.
Data Set 1’s validity could be questioned due to subjective experiences of crime, while Data Set 2’s validity depends on how accurately police define and record crimes, potentially missing unreported crimes.
Data Set 1’s validity could be questioned due to subjective experiences of crime, while Data Set 2’s validity depends on how accurately police define and record crimes, potentially missing unreported crimes.
Problem 3
Question 1
The <- notation is equivalent to an = sign in R and is often used to declare variables. After running this code chunk, the named dataframe df appears in the environment on the right-hand side of RStudio.
df <- read_csv('https://www.openintro.org/data/csv/babies.csv')Rows: 1236 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (8): case, bwt, gestation, parity, age, height, weight, smoke
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Question 2
The notation Hmisc:: directly calls this function from the Hmisc package. describe() is a common function name, and sometimes this is needed to indicate to R which function from which package you want to use. The pipe feature |> sends the results of the first line directly into the function on the 2nd line and is a convenient way to chain functions together.
This code prints a useful and attractive summary of the data set we are using.
Hmisc::describe(df) |>
html()8 Variables 1236 Observations
case
n missing distinct Info Mean Gmd .05 .10 .25
1236 0 1236 1 618.5 412.3 62.75 124.50 309.75
.50 .75 .90 .95
618.50 927.25 1112.50 1174.25
lowest : 1 2 3 4 5 , highest: 1232 1233 1234 1235 1236
bwt
| n | missing | distinct | Info | Mean | Gmd | .05 | .10 | .25 | .50 | .75 | .90 | .95 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1236 | 0 | 107 | 1 | 119.6 | 20.33 | 88.0 | 97.0 | 108.8 | 120.0 | 131.0 | 142.0 | 149.0 |
gestation
| n | missing | distinct | Info | Mean | Gmd | .05 | .10 | .25 | .50 | .75 | .90 | .95 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1223 | 13 | 106 | 0.999 | 279.3 | 16.57 | 252.0 | 262.0 | 272.0 | 280.0 | 288.0 | 295.8 | 302.0 |
parity
| n | missing | distinct | Info | Sum | Mean | Gmd |
|---|---|---|---|---|---|---|
| 1236 | 0 | 2 | 0.57 | 315 | 0.2549 | 0.3801 |
age
| n | missing | distinct | Info | Mean | Gmd | .05 | .10 | .25 | .50 | .75 | .90 | .95 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1234 | 2 | 30 | 0.997 | 27.26 | 6.506 | 19 | 20 | 23 | 26 | 31 | 36 | 38 |
height
| n | missing | distinct | Info | Mean | Gmd | .05 | .10 | .25 | .50 | .75 | .90 | .95 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1214 | 22 | 19 | 0.986 | 64.05 | 2.839 | 60 | 61 | 62 | 64 | 66 | 67 | 68 |
Value 53 54 56 57 58 59 60 61 62 63 64 65
Frequency 1 1 1 1 10 26 55 105 131 166 183 182
Proportion 0.001 0.001 0.001 0.001 0.008 0.021 0.045 0.086 0.108 0.137 0.151 0.150
Value 66 67 68 69 70 71 72
Frequency 153 105 54 20 13 6 1
Proportion 0.126 0.086 0.044 0.016 0.011 0.005 0.001
For the frequency table, variable is rounded to the nearest 0
weight
| n | missing | distinct | Info | Mean | Gmd | .05 | .10 | .25 | .50 | .75 | .90 | .95 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1200 | 36 | 105 | 0.999 | 128.6 | 22.39 | 102.0 | 105.0 | 114.8 | 125.0 | 139.0 | 155.0 | 170.0 |
smoke
| n | missing | distinct | Info | Sum | Mean | Gmd |
|---|---|---|---|---|---|---|
| 1226 | 10 | 2 | 0.717 | 484 | 0.3948 | 0.4782 |
Question 3
The Child Health and Development Studies investigate a range of topics. One study, in particular, considered all pregnancies between 1960 and 1967 among women in the Kaiser Foundation Health Plan in the San Francisco East Bay area. The variables in this data set are as follows.
| Variable Name | Variable Description | Variable Type |
|---|---|---|
case |
id number | |
bwt |
birthweight, in ounces | |
gestation |
length of gestation, in days | |
parity |
binary indicator for a first pregnancy (0 = first pregnancy) | |
age |
mother’s age in years | |
height |
mother’s height in inches | |
weight |
mother’s weight in pounds | |
smoke |
binary indicator for whether the mother smokes |
Question 4
Below, 2 numeric variables were investigated for potential relationships. The independent, explanatory variable I chose is variable_name, and the dependent, response variable I chose is variable_name.
df |>
ggplot(aes(x = gestation, # please change these
y = weight)) +
geom_point()Warning: Removed 48 rows containing missing values or values outside the scale range
(`geom_point()`).
Describe what you see in your plot here.
Session Info
This portion of the document describes the conditions in RStudio under which this report was created. This is important to include so that work is reproducible by others.
xfun::session_info()R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22621)
Locale:
LC_COLLATE=English_United States.utf8
LC_CTYPE=English_United States.utf8
LC_MONETARY=English_United States.utf8
LC_NUMERIC=C
LC_TIME=English_United States.utf8
Package version:
askpass_1.2.0 backports_1.5.0 base64enc_0.1-3
bit_4.0.5 bit64_4.0.5 blob_1.2.4
broom_1.0.6 bslib_0.8.0 cachem_1.1.0
callr_3.7.6 cellranger_1.1.0 checkmate_2.3.2
cli_3.6.3 clipr_0.8.0 cluster_2.1.6
colorspace_2.1-1 compiler_4.4.1 conflicted_1.2.0
cpp11_0.4.7 crayon_1.5.3 curl_5.2.1
data.table_1.15.4 DBI_1.2.3 dbplyr_2.5.0
digest_0.6.37 dplyr_1.1.4 dtplyr_1.3.1
evaluate_0.24.0 fansi_1.0.6 farver_2.1.2
fastmap_1.2.0 fontawesome_0.5.2 forcats_1.0.0
foreign_0.8-86 Formula_1.2-5 fs_1.6.4
gargle_1.5.2 generics_0.1.3 ggplot2_3.5.1
glue_1.7.0 googledrive_2.1.1 googlesheets4_1.1.1
graphics_4.4.1 grDevices_4.4.1 grid_4.4.1
gridExtra_2.3 gtable_0.3.5 haven_2.5.4
highr_0.11 Hmisc_5.1-3 hms_1.1.3
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jquerylib_0.1.4 jsonlite_1.8.8 knitr_1.48
labeling_0.4.3 lattice_0.22.6 lifecycle_1.0.4
lubridate_1.9.3 magrittr_2.0.3 MASS_7.3.60.2
Matrix_1.7.0 memoise_2.0.1 methods_4.4.1
mgcv_1.9.1 mime_0.12 modelr_0.1.11
munsell_0.5.1 nlme_3.1.164 nnet_7.3-19
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rlang_1.1.4 rmarkdown_2.28 rpart_4.1.23
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scales_1.3.0 selectr_0.4.2 splines_4.4.1
stats_4.4.1 stringi_1.8.4 stringr_1.5.1
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tidyverse_2.0.0 timechange_0.3.0 tinytex_0.52
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withr_3.0.1 xfun_0.47 xml2_1.3.6
yaml_2.3.10